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Published on: May 10, 2013
Multicommutated flow analysis system for determination of creatinine in physiological fluids by Jaffe method
Łukasz Tymecki1, Jakub Korszun, Kamil Strzelak
1University of Warsaw, Department of Chemistry, Warsaw, Poland. luktym@chem.uw.edu.pl
A new paired emitter detector diode (PEDD) system enables precise photometric creatinine determination using the Jaffe protocol. This advanced analytical system accurately measures creatinine in human urine and serum samples.
Area of Science:
- Analytical Chemistry
- Biomedical Engineering
- Clinical Diagnostics
Background:
- Photometric determination of creatinine is crucial for clinical diagnostics.
- Existing methods may face limitations in sensitivity, throughput, or sample handling.
- The Jaffe protocol is a widely used method for creatinine measurement.
Purpose of the Study:
- To develop a novel paired emitter detector diode (PEDD) detector for enhanced photometric creatinine determination.
- To integrate the PEDD detector into a multicommutated flow analysis (MCFA) system for automated analysis.
- To evaluate the performance of the developed system for creatinine measurement in real biological samples.
Main Methods:
- Development of a compact flow-through cell with integrated 505 nm LED emitter and 525 nm LED detector.
- Construction of a fully mechanized MCFA system controlled by a microprocessor.
- Application of a multi-point fixed-time kinetic measurement procedure.
Main Results:
- The developed PEDD-based MCFA system achieved submillimolar creatinine determination with a parts per million (ppm) detection limit.
- The system demonstrated a sample throughput of 15-40 samples per hour.
- Successful application for creatinine determination in human urine and serum samples, unaffected by sample viscosity.
Conclusions:
- The developed PEDD-based MCFA system offers a sensitive, efficient, and robust platform for photometric creatinine analysis.
- This automated system provides accurate results for real-world clinical samples.
- The system's design addresses challenges related to sample viscosity, enhancing its applicability.
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